Chaos control using maximum Lyapunov number of universal learning network

K. Hirasawa, X. Wan, J. Murata, J. Hu

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

Chaotic behaviors are characterized mainly by Lyapunov numbers of a dynamic system. In this paper, a new method is proposed, which can control the maximum Lyapunov number of dynamic system that can be represented by Universal Learning Networks (ULNs). The maximum Lyapunov number of a dynamic system can be formulated by using higher order derivatives of ULNs and parameters of ULNs can be adjusted for the maximum Lyapunov number to approach to the target value by the combined gradient and random search method. Based on simulation results, a fully connected ULN with three nodes is possible to display chaotic behaviors.

Original languageEnglish
Pages (from-to)1702-1707
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume2
Publication statusPublished - Dec 1 1998
EventProceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 2 (of 5) - San Diego, CA, USA
Duration: Oct 11 1998Oct 14 1998

Fingerprint

chaotic dynamics
Chaos theory
Dynamical systems
learning
Derivatives
simulation
method

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture

Cite this

Chaos control using maximum Lyapunov number of universal learning network. / Hirasawa, K.; Wan, X.; Murata, J.; Hu, J.

In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 2, 01.12.1998, p. 1702-1707.

Research output: Contribution to journalConference article

@article{0de919755d0a409f8f8cfcd079b24130,
title = "Chaos control using maximum Lyapunov number of universal learning network",
abstract = "Chaotic behaviors are characterized mainly by Lyapunov numbers of a dynamic system. In this paper, a new method is proposed, which can control the maximum Lyapunov number of dynamic system that can be represented by Universal Learning Networks (ULNs). The maximum Lyapunov number of a dynamic system can be formulated by using higher order derivatives of ULNs and parameters of ULNs can be adjusted for the maximum Lyapunov number to approach to the target value by the combined gradient and random search method. Based on simulation results, a fully connected ULN with three nodes is possible to display chaotic behaviors.",
author = "K. Hirasawa and X. Wan and J. Murata and J. Hu",
year = "1998",
month = "12",
day = "1",
language = "English",
volume = "2",
pages = "1702--1707",
journal = "Proceedings of the IEEE International Conference on Systems, Man and Cybernetics",
issn = "0884-3627",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Chaos control using maximum Lyapunov number of universal learning network

AU - Hirasawa, K.

AU - Wan, X.

AU - Murata, J.

AU - Hu, J.

PY - 1998/12/1

Y1 - 1998/12/1

N2 - Chaotic behaviors are characterized mainly by Lyapunov numbers of a dynamic system. In this paper, a new method is proposed, which can control the maximum Lyapunov number of dynamic system that can be represented by Universal Learning Networks (ULNs). The maximum Lyapunov number of a dynamic system can be formulated by using higher order derivatives of ULNs and parameters of ULNs can be adjusted for the maximum Lyapunov number to approach to the target value by the combined gradient and random search method. Based on simulation results, a fully connected ULN with three nodes is possible to display chaotic behaviors.

AB - Chaotic behaviors are characterized mainly by Lyapunov numbers of a dynamic system. In this paper, a new method is proposed, which can control the maximum Lyapunov number of dynamic system that can be represented by Universal Learning Networks (ULNs). The maximum Lyapunov number of a dynamic system can be formulated by using higher order derivatives of ULNs and parameters of ULNs can be adjusted for the maximum Lyapunov number to approach to the target value by the combined gradient and random search method. Based on simulation results, a fully connected ULN with three nodes is possible to display chaotic behaviors.

UR - http://www.scopus.com/inward/record.url?scp=0032307951&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032307951&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0032307951

VL - 2

SP - 1702

EP - 1707

JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

SN - 0884-3627

ER -